Multiplatform Radar Detection Generation

This example shows how to generate radar detections from a multiplatform radar network. The network includes three long-range platforms: two airborne and one ground-based. Such synthetic data can be used to test the performance of tracking architectures for different target types and maneuvers.

The radar platforms and targets are modeled in the scenario as platforms. Simulation of the motion of the platforms in the scenario is managed by trackingScenario.

Add a planar array radar to the platform. Mount the radar in a radome 5 meters above the platform. Model the radar as a mechanically rotating phased array. The radar electronically stacks beams in elevation along the array's boresight. The specifications for the modeled radar are tabulated below:

Multiple sensors can be mounted on a platform. Add a radar composed of two linear phased arrays mounted 5 meters above the platform. Mount the arrays so that one array looks over the right side of the airframe and the other array looks over the left side of the airframe. Both arrays provide coverage over a 150 degree azimuth sector on either side of the platform. Elevation is not measured by the linear arrays. The specifications for this radar are tabulated below:

Notice the wide beams from the airborne platforms and the narrow beam from the ground-based radar executing a raster scan. You can visualize the ground truth trajectories in the 2D view below. The four targets are represented by triangles. Around 30 km on the x-axis is the airliner traveling east (left to right). Around 2 km on the x-axis is the jet executing a turn clockwise. Further south are two crossing airliners.

view(-90,90); % 2D view

Plot the logged detections with their measurement uncertainties. Each color corresponds to the platform generating the detections. The legend from the previous display applies to all the following plots. Notice that the radars generate false alarms, which are detection far away from the target trajectories.

The following 3D view shows how these detections are distributed in elevation. For platforms with 3D sensors (the blue and yellow platforms), the detections closely follow the target trajectories. The 2D-view platform's detections (the red platform) are offset in elevation from the target trajectories because its radar is unable to measure in elevation. The 1-sigma measurement uncertainty is shown for each detection as a gray ellipsoid centered on the measured target positions (shown as filled circles).

view([-60 25]); % 3D view

Zoom in on the jet executing the 90 degree horizontal turn. The 1-sigma measurement uncertainty is reported by the radar according to the radar's resolution and the signal-to-noise ratio (SNR) for each detection. Targets at longer ranges or with smaller SNR values will have larger measurement uncertainties than targets at closer ranges or with larger SNR values. Notice that the blue detections have smaller measurement uncertainties than the yellow detections. This is because the blue detections originate from the airborne platform (Platform 1) that is much closer to the target than the ground-based platform (Platform 3) generating the yellow detections.

Notice the large uncertainty in elevation of the red detections generating from the airborne platform (Platform 2) that uses two linear arrays. The ellipsoids have small axes in the range and azimuth directions but have very large axes along the elevation direction. This is because the linear arrays on this platform are unable to provide estimates in elevation. In this case, the platform's radar reports detections at 0 degrees with an uncertainty in elevation corresponding to the elevation field of view.

Zoom in on the two crossing airliners. The blue airborne radar with the rotating array generates the fewest number of detections (only 4 detections for these two targets), but these detections are the most precise (smallest ellipses). The small number of detections from this platform is due to its radar's 360 mechanical scan, which limits how frequently its beam can revisit a target in the scenario. The other platforms have radars with smaller scan regions, allowing them to revisit the targets at a higher rate.

Summary

This example shows how to model a radar surveillance network and simulate detections generated by multiple airborne and ground-based radar platforms. In this example, you learned how to define scenarios, including targets and platforms that can be stationary or in motion. You also learned how to visualize the ground truth trajectories, sensor beams, detections, and associated measurement uncertainties. You can process this synthetic data through your tracking and fusion algorithms to assess their performance for this scenario. You can also modify this example to exercise your multi-target tracker against different target types and maneuvers.

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